In the supervised setting, approaches have been developed to explain the predictions of machine learning models. A popular approach is class activation mapping (CAM). CAM uses a linear combination of the activation channels, or maps, from a particular convolutional layer to identify the discriminative regions from the original image used to predict a particular category [1]}. The original CAM uses a global average pooling (GAP) layer prior to the softmax to obtain the weights of each of the activation channels. However, the incorporation of a GAP layer may result in a decrease in performance. Thus, other methods, which do not require the GAP layer, have been developed [2]}, [3]}. One such method, Score-CAM, calculates the weight of each activation channel using a global contribution of the corresponding input features [3]}. Score-CAM has been shown to achieve better performance for discriminative localization for classification than existing methods [3]}. However, none of the CAM methods have been applied in the unsupervised deep clustering framework. In this work, we develop novel methodology to generate CLuster Activation Mapping (CLAM) which combines an unsupervised deep clustering framework with a modification of Score-CAM, an approach for discriminative localization in the supervised setting.
